The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Discovery and characterization of chromatin states for systematic annotation of the human genome
Open accessAbstract
A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation and the onset of disease. Although individual modifications have been linked to the activity levels of various genetic functional elements, their combinatorial patterns are still unresolved and their potential for systematic de novo genome annotation remains untapped. Here, we use a multivariate Hidden Markov Model to reveal 'chromatin states' in human T cells, based on recurrent and spatially coherent combinations of chromatin marks. We define 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, large-scale repressed and repeat-associated states. Each chromatin state shows specific enrichments in functional annotations, sequence motifs and specific experimentally observed characteristics, suggesting distinct biological roles. This approach provides a complementary functional annotation of the human genome that reveals the genome-wide locations of diverse classes of epigenetic function.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Chromatin modifications and their function
Kouzarides, T. (2007) · Cell · doi:10.1016/j.cell.2007.02.005 · Cited by 10,883
The Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400
High-Resolution Profiling of Histone Methylations in the Human Genome
Barski, A., Cuddapah, S., Cui, K., Roh, T.-Y., Schones, D. E., Wang, Z., Wei, G., Chepelev, I., & Zhao, K. (2007) · Cell · doi:10.1016/j.cell.2007.05.009 · Cited by 6,845
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223
A second generation human haplotype map of over 3.1 million SNPs
The International HapMap Consortium (2007) · Nature · doi:10.1038/nature06258 · Cited by 4,438
Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals
Guttman, M., Amit, I., Garber, M., French, C., Lin, M. F., Feldser, D., Huarte, M., Zuk, O., Carey, B. W., Cassady, J. P., et al. (2009) · Nature · doi:10.1038/nature07672 · Cited by 4,098
Self-citationEvolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes
Siepel, A., Bejerano, G., Pedersen, J. S., Hinrichs, A. S., Hou, M., Rosenbloom, K., Clawson, H., Spieth, J., Hillier, L. W., Richards, S., et al. (2005) · Genome Research · doi:10.1101/gr.3715005 · Cited by 3,971
A gene atlas of the mouse and human protein-encoding transcriptomes
Su, A. I., Wiltshire, T., Batalov, S., Lapp, H., Ching, K. A., Block, D., Zhang, J., Soden, R., Hayakawa, M., Kreiman, G., et al. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0400782101 · Cited by 3,475
Heintzman, N. D., Stuart, R. K., Hon, G., Fu, Y., Ching, C. W., Hawkins, R. D., Barrera, L. O., Van Calcar, S., Qu, C., Ching, K. A., et al. (2007) · Nature Genetics · doi:10.1038/ng1966 · Cited by 3,398
Histone modifications at human enhancers reflect global cell-type-specific gene expression
Heintzman, N. D., Hon, G. C., Hawkins, R. D., Kheradpour, P., Stark, A., Harp, L. F., Ye, Z., Lee, L. K., Stuart, R. K., Ching, C. W., et al. (2009) · Nature · doi:10.1038/nature07829 · Cited by 2,533
Self-citation
Cited by
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
Self-citationMaster Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes
Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., Rahl, P. B., Lee, T. I., & Young, R. A. (2013) · Cell · doi:10.1016/j.cell.2013.03.035 · Cited by 4,143
Annotation of functional variation in personal genomes using RegulomeDB
Boyle, A. P., Hong, E. L., Hariharan, M., Cheng, Y., Schaub, M. A., Kasowski, M., Karczewski, K. J., Park, J., Hitz, B. C., Weng, S., et al. (2012) · Genome Research · doi:10.1101/gr.137323.112 · Cited by 2,722
Mapping and analysis of chromatin state dynamics in nine human cell types
Ernst, J., Kheradpour, P., Mikkelsen, T. S., Shoresh, N., Ward, L. D., Epstein, C. B., Zhang, X., Wang, L., Issner, R., Coyne, M., et al. (2011) · Nature · doi:10.1038/nature09906 · Cited by 2,898
Self-citationChIP-seq guidelines and practices of the ENCODE and modENCODE consortia
Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B. E., Bickel, P., Brown, J. B., Cayting, P., et al. (2012) · Genome Research · doi:10.1101/gr.136184.111 · Cited by 2,033
Self-citationEpigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity
Saeed, S., Quintin, J., Kerstens, H. H. D., Rao, N. A., Aghajanirefah, A., Matarese, F., Cheng, S.-C., Ratter, J., Berentsen, K., van der Ent, M. A., et al. (2014) · Science · doi:10.1126/science.1251086 · Cited by 1,761
Charting histone modifications and the functional organization of mammalian genomes
Zhou, V. W., Goren, A., & Bernstein, B. E. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2905 · Cited by 1,147
A high-resolution map of human evolutionary constraint using 29 mammals
Broad Institute Sequencing Platform and Whole Genome Assembly Team, Lindblad-Toh, K., Baylor College of Medicine Human Genome Sequencing Center Sequencing Team, Genome Institute at Washington University, Garber, M., Zuk, O., Lin, M. F., Parker, B. J., Washietl, S., Kheradpour, P., et al. (2011) · Nature · doi:10.1038/nature10530 · Cited by 1,108
Self-citationThe genetic architecture of type 2 diabetes
Fuchsberger, C., Flannick, J., Teslovich, T. M., Mahajan, A., Agarwala, V., Gaulton, K. J., Ma, C., Fontanillas, P., Moutsianas, L., McCarthy, D. J., et al. (2016) · Nature · doi:10.1038/nature18642 · Cited by 1,051
Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci
De Jager, P. L., Srivastava, G., Lunnon, K., Burgess, J., Schalkwyk, L. C., Yu, L., Eaton, M. L., Keenan, B. T., Ernst, J., McCabe, C., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3786 · Cited by 956
“Using histone modification profiles and established methods 27 , each 200 bp segment of the genome is annotated as being in one of 11 chromatin states ( Figure 3a ) that capture the transcriptional states and putative regulatory elements found in this tissue.”
Self-citationThe selection and function of cell type-specific enhancers
Heinz, S., Romanoski, C. E., Benner, C., & Glass, C. K. (2015) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3949 · Cited by 1,010
Parker, S. C. J., Stitzel, M. L., Taylor, D. L., Orozco, J. M., Erdos, M. R., Akiyama, J. A., van Bueren, K. L., Chines, P. S., Narisu, N., Program, N. C. S., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1317023110 · Cited by 665
Genome Architecture: Domain Organization of Interphase Chromosomes
Bickmore, W. A., & van Steensel, B. (2013) · Cell · doi:10.1016/j.cell.2013.02.001 · Cited by 725
Chromatin accessibility profiling by ATAC-seq
Grandi, F. C., Modi, H., Kampman, L., & Corces, M. R. (2022) · Nature Protocols · doi:10.1038/s41596-022-00692-9 · Cited by 557
Sequential regulatory activity prediction across chromosomes with convolutional neural networks
Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research · doi:10.1101/gr.227819.117 · Cited by 537
Histone modification: cause or cog?
Henikoff, S., & Shilatifard, A. (2011) · Trends in Genetics · doi:10.1016/j.tig.2011.06.006 · Cited by 482
Passive and active DNA methylation and the interplay with genetic variation in gene regulation
Gutierrez-Arcelus, M., Lappalainen, T., Montgomery, S. B., Buil, A., Ongen, H., Yurovsky, A., Bryois, J., Giger, T., Romano, L., Planchon, A., et al. (2013) · eLife · doi:10.7554/elife.00523 · Cited by 440
Paternal Diet Defines Offspring Chromatin State and Intergenerational Obesity
Öst, A., Lempradl, A., Casas, E., Weigert, M., Tiko, T., Deniz, M., Pantano, L., Boenisch, U., Itskov, P. M., Stoeckius, M., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.005 · Cited by 406
Ram, O., Goren, A., Amit, I., Shoresh, N., Yosef, N., Ernst, J., Kellis, M., Gymrek, M., Issner, R., Coyne, M., et al. (2011) · Cell · doi:10.1016/j.cell.2011.09.057 · Cited by 331
Self-citationKheradpour, P., Ernst, J., Melnikov, A., Rogov, P., Wang, L., Zhang, X., Alston, J., Mikkelsen, T. S., & Kellis, M. (2013) · Genome Research · doi:10.1101/gr.144899.112 · Cited by 315
Self-citationMicroRNA-148a regulates LDL receptor and ABCA1 expression to control circulating lipoprotein levels
Goedeke, L., Rotllan, N., Canfrán-Duque, A., Aranda, J. F., Ramírez, C. M., Araldi, E., Lin, C.-S., Anderson, N. N., Wagschal, A., de Cabo, R., et al. (2015) · Nature Medicine · doi:10.1038/nm.3949 · Cited by 238
Evidence of Abundant Purifying Selection in Humans for Recently Acquired Regulatory Functions
Ward, L. D., & Kellis, M. (2012) · Science · doi:10.1126/science.1225057 · Cited by 209
Self-citationHigh-throughput functional testing of ENCODE segmentation predictions
Kwasnieski, J. C., Fiore, C., Chaudhari, H. G., & Cohen, B. A. (2014) · Genome Research · doi:10.1101/gr.173518.114 · Cited by 224
Wang, X., He, L., Goggin, S. M., Saadat, A., Wang, L., Sinnott-Armstrong, N., Claussnitzer, M., & Kellis, M. (2018) · Nature Communications · doi:10.1038/s41467-018-07746-1 · Cited by 159
Self-citationvel Szic, K. S., Declerck, K., Vidaković, M., & Vanden Berghe, W. (2015) · Clinical Epigenetics · doi:10.1186/s13148-015-0068-2 · Cited by 192
Ubiquitous heterogeneity and asymmetry of the chromatin environment at regulatory elements
Kundaje, A., Kyriazopoulou-Panagiotopoulou, S., Libbrecht, M., Smith, C. L., Raha, D., Winters, E. E., Johnson, S. M., Snyder, M., Batzoglou, S., & Sidow, A. (2012) · Genome Research · doi:10.1101/gr.136366.111 · Cited by 178
Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions
Ernst, J., Melnikov, A., Zhang, X., Wang, L., Rogov, P., Mikkelsen, T. S., & Kellis, M. (2016) · Nature Biotechnology · doi:10.1038/nbt.3678 · Cited by 162
Self-citationGrossman, S. R., Zhang, X., Wang, L., Engreitz, J., Melnikov, A., Rogov, P., Tewhey, R., Isakova, A., Deplancke, B., Bernstein, B. E., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1621150114 · Cited by 165
Transcriptional response to stress is pre-wired by promoter and enhancer architecture
Vihervaara, A., Mahat, D. B., Guertin, M. J., Chu, T., Danko, C. G., Lis, J. T., & Sistonen, L. (2017) · Nature Communications · doi:10.1038/s41467-017-00151-0 · Cited by 164
Chen, H., Tian, Y., Shu, W., Bo, X., & Wang, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041374 · Cited by 161
Ulianov, S. V., Velichko, A. K., Magnitov, M. D., Luzhin, A. V., Golov, A. K., Ovsyannikova, N., Kireev, I. I., Gavrikov, A. S., Mishin, A. S., Garaev, A. K., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab249 · Cited by 137
Nobles, C. L., Sherrill-Mix, S., Everett, J. K., Reddy, S., Fraietta, J. A., Porter, D. L., Frey, N., Gill, S. I., Grupp, S. A., Maude, S. L., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci130144 · Cited by 130
Gisselbrecht, S. S., Palagi, A., Kurland, J. V., Rogers, J. M., Ozadam, H., Zhan, Y., Dekker, J., & Bulyk, M. L. (2020) · Molecular Cell · doi:10.1016/j.molcel.2019.10.004 · Cited by 128
Dynamics of the epigenetic landscape during erythroid differentiation after GATA1 restoration
Wu, W., Cheng, Y., Keller, C. A., Ernst, J., Kumar, S. A., Mishra, T., Morrissey, C., Dorman, C. M., Chen, K.-B., Drautz, D., et al. (2011) · Genome Research · doi:10.1101/gr.125088.111 · Cited by 118
Self-citationDEEP: a general computational framework for predicting enhancers
Kleftogiannis, D., Kalnis, P., & Bajic, V. B. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1058 · Cited by 125
Histone deacetylase 3 associates with MeCP2 to regulate FOXO and social behavior
Nott, A., Cheng, J., Gao, F., Lin, Y.-T., Gjoneska, E., Ko, T., Minhas, P., Zamudio, A. V., Meng, J., Zhang, F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4347 · Cited by 110
Zacher, B., Michel, M., Schwalb, B., Cramer, P., Tresch, A., & Gagneur, J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169249 · Cited by 91
Integrative genomic analysis reveals widespread enhancer regulation by p53 in response to DNA damage
Younger, S. T., Kenzelmann-Broz, D., Jung, H., Attardi, L. D., & Rinn, J. L. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv284 · Cited by 99
Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro
Zhang, B., Srivastava, A., Mimitou, E., Stuart, T., Raimondi, I., Hao, Y., Smibert, P., & Satija, R. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01250-0 · Cited by 104
Epigenetic changes at gene promoters in response to immune activation in utero
Tang, B., Jia, H., Kast, R. J., & Thomas, E. A. (2013) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2013.01.086 · Cited by 91
Libbrecht, M. W., Ay, F., Hoffman, M. M., Gilbert, D. M., Bilmes, J. A., & Noble, W. S. (2015) · Genome Research · doi:10.1101/gr.184341.114 · Cited by 84
Porcellini, E., Laprovitera, N., Riefolo, M., Ravaioli, M., Garajova, I., & Ferracin, M. (2018) · Cancer Letters · doi:10.1016/j.canlet.2018.01.049 · Cited by 66
Yen, A., & Kellis, M. (2015) · Nature Communications · doi:10.1038/ncomms8973 · Cited by 67
Self-citationGenetic Networks Governing Heart Development
Waardenberg, A. J., Ramialison, M., Bouveret, R., & Harvey, R. P. (2014) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a013839 · Cited by 59
Chamorro-Jorganes, A., Araldi, E., Rotllan, N., Cirera-Salinas, D., & Suárez, Y. (2014) · Journal of Cell Science · doi:10.1242/jcs.130518 · Cited by 57
Epigenetic regulation of key vascular genes and growth factors
Turunen, M. P., & Yla-Herttuala, S. (2011) · Cardiovascular Research · doi:10.1093/cvr/cvr109 · Cited by 41
Widespread Chromatin Accessibility at Repetitive Elements Links Stem Cells with Human Cancer
Gomez, N. C., Hepperla, A. J., Dumitru, R., Simon, J. M., Fang, F., & Davis, I. J. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.10.011 · Cited by 41
ChAsE: chromatin analysis and exploration tool
Younesy, H., Nielsen, C. B., Lorincz, M. C., Jones, S. J. M., Karimi, M. M., & Möller, T. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw382 · Cited by 41
Universal chromatin state annotation of the mouse genome
Vu, H., & Ernst, J. (2023) · Genome Biology · doi:10.1186/s13059-023-02994-x · Cited by 37
Self-citationEpigenetic regulation of transcriptional plasticity associated with developmental song learning
Kelly, T. K., Ahmadiantehrani, S., Blattler, A., & London, S. E. (2018) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2018.0160 · Cited by 30
Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform
Madrigal, P., & Krajewski, P. (2015) · BioData Mining · doi:10.1186/s13040-015-0051-7 · Cited by 15
Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states
Li, W. V., Razaee, Z. S., & Li, J. J. (2016) · BMC Genomics · doi:10.1186/s12864-015-2303-9 · Cited by 10
Interpreting noncoding genetic variation in complex traits and human disease
Ward, L. D., & Kellis, M. (2012) · Nature Biotechnology · doi:10.1038/nbt.2422 · Cited by 6
Self-citation
Metadata
- Authors
- Ernst, J., & Kellis, M.
- Venue
- Nature Biotechnology
- Published
- 2010-07-25
- Volume
- 28
- Issue
- 8
- Pages
- 817-825
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2044826047
- Cited by
- 1,056
- References
- 19
- FWCI
- 32.49
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
